Titlebook: Computer Aided Verification; 32nd International C Shuvendu K. Lahiri,Chao Wang Conference proceedings‘‘‘‘‘‘‘‘ 2020 The Editor(s) (if applic

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Code2Inv: A Deep Learning Framework for Program Verificatione former encodes the verification task into numeric vectors while the latter describes the format of solutions Code2Inv should produce. We demonstrate the flexibility of Code2Inv by means of two small-scale yet expressive instances: a loop invariant synthesizer for C programs, and a Constrained Horn Clause (CHC) solver.

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Local Reasoning About the Presence of Bugs: Incorrectness Separation Logictheory of this new . (ISL), and use it to derive a begin-anywhere, intra-procedural symbolic execution analysis that has no false positives .. In so doing, we take a step towards transferring modular, scalable techniques from the world of program verification to bug catching.

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0302-9743 lockchain and Security; Concurrency; hardware verification and decision procedures; and hybrid and dynamic systems..Part II: model checking; software verification; stochastic systems; and synthesis.. .*The conference was held virtually due to the COVID-19 pandemic..978-3-030-53290-1978-3-030-53291-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Action-Based Model Checking: Logic, Automata, and ReductionBA can be transformed to an . that can be used in an on-the-fly POR algorithm. We have implemented these algorithms in a new model checker named ., and demonstrate their effectiveness using the RERS 2019 benchmark suite.